Other applications beyond head and hand interactions are also under
investigation. We shall enumerate a few concepts and leave it to the
reader's imagination to conjure other scenarios. One of the virtues
about the ARL system and its perceptual, data-driven nature is that it
is flexible. There are no explicit mechanisms here to exclusively
learn head and hand dynamics. The only systems that were specific to
head and hand motion were the vision and graphics system. The time
series processing and learning algorithm did not specifically require
such types of data. In the learning system, there were no cognitive or
kinematic models that concerned gesticulations or hand gesture
constraints. Although such models could have been helpful additions in
this scenario, they could be detrimental when the ARL learns a
different modality (i.e. facial motion). The lack of such hard-wired
models increases the generality of the approach. Thus, a large space
of time varying measurements and outputs can be handled and the
behaviours recovered could have had a markedly different structure.